Vorlesung: Deep Learning for Visual Recognition

Veranstaltung

  • Dozent(en):
  • Beginn: 19.10.2016
  • Zeiten: Wed. 10:30 - 12:00, LBH / HS III.a
  • Veranstaltungsnummer: MA-INF 2313
  • Studiengang: Master
  • Aufwand: 6 CP
  • Prüfungen: date tbd

Übung

Beschreibung

Neural networks are making a comeback!  Deep learning has taken over the machine learning community by storm, with success both in research and commercially.  Deep learning is applicable over a range of fields such as computer vision, speech recognition, natural language processing, robotics, etc.  This course will introduce the fundamentals of neural networks and then progress to state-of-the-art convolutional and recurrent neural networks as well as their use in applications for visual recognition.  Students will get a chance to learn how to implement and train their own network for visual recognition tasks such as object recognition, image segmentation and caption generation.

No formal pre-requisites.  Students should already be comfortable with concepts in probability theory and optimization and are recommended to have taken at least one course in machine learning or computer vision.  Exercises will be a mix of theory and practical (Python).

News

Please note that there will be double lecture on Nov. 30, with the second lecture taking place in the exercise slot.  Course projects will also be announced on this day in the exercise slot.  Please make sure you are present to sign up for a project and a presentation date.  The projects will be allocated on a first come first serve basis.  If you are unable to attend, please email the TAs to make individual arrangements.

First lecture starts on October 19, 2016.  There will be no exercises on the first day.  See you there!

Weitere Dokumente

Folien

Übungsaufgaben

Übung 1: MLbasics
Übungsblatt  (PDF-Dokument, 157 KB)
Übung 2: Theano
Übungsblatt  (einfaches Textdokument, 30 Bytes)
Übung 3: NNintro
Übungsblatt  (PDF-Dokument, 109 KB)
Übung 4: Optimization
Übungsblatt  (PDF-Dokument, 139 KB)